High-throughput, label-free, single-cell, microalgal lipid screening by machine-learning-equipped optofluidic...

Area of Science:

• Biotechnology
• Renewable Energy
• Microscopy

Background:

• Petroleum-based fuels face sustainability challenges, driving demand for alternative sources like microalgal biofuel.
• Microalgae absorb atmospheric CO2, offering a climate change mitigation pathway.
• Current methods for analyzing microalgal lipids lack single-cell resolution and are often invasive.

Purpose of the Study:

• To develop a high-throughput, label-free method for single-cell analysis of lipid-producing microalgae.
• To characterize heterogeneous populations of Euglena gracilis for biofuel applications.
• To enable non-invasive, interference-free assessment of microalgal lipid content.

Main Methods:

• Optofluidic time-stretch quantitative phase microscopy integrated with hydrodynamic focusing and machine learning.
• High-throughput screening of individual microalgal cells at 10,000 cells/s.
• Label-free generation of opacity and phase maps for cell classification.

Main Results:

• Demonstrated label-free, single-cell screening of lipid-producing microalgae.
• Successfully characterized Euglena gracilis populations under varying nutrient conditions.
• Achieved high-throughput cell classification with a low error rate of 2.15%.

Conclusions:

• The developed optofluidic microscope is an effective analytical tool for microalgae-based biofuel production.
• This method provides accurate, non-invasive single-cell characterization crucial for optimizing biofuel yields.
• The technology supports the advancement of sustainable and economical biofuel alternatives.